Case study
AI mentoring and coaching,
matched and measured
We built an AI mentoring and coaching platform that pairs people by psychosocial fit and then proves the pairing worked, tracking real progress against KPIs on a Node.js and Moleculer microservices backend built to scale each part on its own.
At a glance
The engineering
The hard part was matching people well, then proving it worked
Mentoring software is easy to build and hard to make effective. Two things decided whether a match actually helped: who got paired with whom, and whether anyone could see progress.
Psychosocial matching, not a lookup
Pairing a mentor and a mentee well is more than filtering by tags. We modeled psychosocial fit so matches are made on the factors that actually predict a productive relationship.
Progress you can measure
A mentoring program that cannot show outcomes gets cut. We built KPI-based tracking so mentees, mentors, and program owners see movement against real goals, not just a count of sessions.
A backend that scales by part
Matching, tracking, and the AI layer each grow at different rates. A Moleculer microservices architecture lets every service scale on its own, so load on one never drags the rest down.
Scope
What we built
Built with
The stack
Where it landed
Let's build yours
Our team contacts you within 24 business hours
We collect all the key requirements from you
The team of developers prepares estimation
We can sign NDA since we respect the confidentiality of our clients